import pandas as pd
import numpy as np
import igraph as ig
import collections
import matplotlib
%matplotlib inline

Explore Data

df_unweighted = pd.read_csv('combined_final_unweighted.csv')
df_unweighted

	node_start	node_end	status
0	Hong Kong	Samoa	jurisdiction
1	Hong Kong	Samoa	jurisdiction
2	Hong Kong	Samoa	jurisdiction
3	Hong Kong	Samoa	jurisdiction
4	Hong Kong	Samoa	jurisdiction
...	...	...	...
338127	Indonesia	British Virgin Islands	officer_base
338128	Cook Islands	British Virgin Islands	officer_base
338129	Hong Kong	British Virgin Islands	officer_base
338130	Cook Islands	United Kingdom	officer_base
338131	Hong Kong	United Kingdom	officer_base

338132 rows × 3 columns

df_weighted = pd.read_csv('combined_final_weighted.csv')
df_weighted

	node_start	node_end	status	weight
0	Hong Kong	Samoa	jurisdiction	2.0
1	Hong Kong	Samoa	jurisdiction	2.0
2	Hong Kong	Samoa	jurisdiction	2.0
3	Hong Kong	Samoa	jurisdiction	2.0
4	Hong Kong	Samoa	jurisdiction	2.0
...	...	...	...	...
338119	Indonesia	British Virgin Islands	officer_base	1.0
338120	Cook Islands	British Virgin Islands	officer_base	1.0
338121	Hong Kong	British Virgin Islands	officer_base	1.0
338122	Cook Islands	United Kingdom	officer_base	1.0
338123	Hong Kong	United Kingdom	officer_base	1.0

338124 rows × 4 columns

df_weighted['status'].unique()

array(['jurisdiction', 'intermediary_base', 'intermediary_jurisdiction',
       'officer_base', 'officer_jurisdiction'], dtype=object)

jurisdiction_status_list = ['jurisdiction', 'intermediary_jurisdiction', 'officer_jurisdiction']

df_weighted_jur = df_weighted[df_weighted['status'].isin(jurisdiction_status_list)]
df_jur = df_weighted_jur.drop(['status'], axis = 1)
df_jur

	node_start	node_end	weight
0	Hong Kong	Samoa	2.0
1	Hong Kong	Samoa	2.0
2	Hong Kong	Samoa	2.0
3	Hong Kong	Samoa	2.0
4	Hong Kong	Samoa	2.0
...	...	...	...
221140	China	British Virgin Islands	0.5
221175	New Zealand	British Virgin Islands	0.5
221176	Cook Islands	British Virgin Islands	0.5
221177	Singapore	British Virgin Islands	0.5
221178	Russia	Singapore	0.5

218952 rows × 3 columns

df_jur_grouped = df_jur.groupby(['node_start','node_end'], as_index = False).sum()
df_jur_grouped

	node_start	node_end	weight
0	Albania	Bahamas	4.0
1	Andorra	Bahamas	52.0
2	Andorra	British Virgin Islands	78.0
3	Andorra	Cayman Islands	2.0
4	Andorra	Cook Islands	0.5
...	...	...	...
1293	Vietnam	Panama	2.0
1294	Yemen	Panama	2.0
1295	Zambia	British Virgin Islands	4.0
1296	Zimbabwe	British Virgin Islands	10.0
1297	Zimbabwe	Panama	6.0

1298 rows × 3 columns

Convert Data to an Undirected Edge List

df_jur_grouped_list = df_jur_grouped.values.tolist()
df_jur_grouped_list

[['Albania', 'Bahamas', 4.0],
 ['Andorra', 'Bahamas', 52.0],
 ['Andorra', 'British Virgin Islands', 78.0],
 ['Andorra', 'Cayman Islands', 2.0],
 ['Andorra', 'Cook Islands', 0.5],
 ['Andorra', 'Costa Rica', 6.0],
 ['Andorra', 'Hong Kong', 18.0],
 ['Andorra', 'Nevada', 48.0],
 ['Andorra', 'Niue', 24.0],
 ['Andorra', 'Panama', 558.0],
 ['Andorra', 'Samoa', 18.0],
 ['Andorra', 'Seychelles', 144.0],
 ['Andorra', 'United Kingdom', 2.0],
 ['Andorra', 'Uruguay', 30.0],
 ['Angola', 'Bermuda', 2.0],
 ['Angola', 'British Virgin Islands', 22.0],
 ['Antigua and Barbuda', 'Barbados', 16.0],
 ['Antigua and Barbuda', 'British Virgin Islands', 3.5],
 ['Antigua and Barbuda', 'Cayman Islands', 2.0],
 ['Antigua and Barbuda', 'Niue', 4.0],
 ['Antigua and Barbuda', 'Panama', 30.0],
 ['Antigua and Barbuda', 'Saint Kitts and Nevis', 2.0],
 ['Antigua and Barbuda', 'Samoa', 0.5],
 ['Antigua and Barbuda', 'Seychelles', 6.0],
 ['Argentina', 'Bahamas', 46.0],
 ['Argentina', 'Barbados', 2.0],
 ['Argentina', 'British Virgin Islands', 166.0],
 ['Argentina', 'Cayman Islands', 6.0],
 ['Argentina', 'Costa Rica', 2.0],
 ['Argentina', 'Hong Kong', 8.0],
 ['Argentina', 'Nevada', 8.0],
 ['Argentina', 'New Zealand', 2.0],
 ['Argentina', 'Niue', 4.0],
 ['Argentina', 'Panama', 292.0],
 ['Argentina', 'Seychelles', 12.0],
 ['Aruba', 'Barbados', 2.0],
 ['Aruba', 'British Anguilla', 8.0],
 ['Aruba', 'British Virgin Islands', 38.0],
 ['Aruba', 'Panama', 8.0],
 ['Australia', 'Bahamas', 4.0],
 ['Australia', 'Barbados', 4.0],
 ['Australia', 'Bermuda', 84.0],
 ['Australia', 'British Anguilla', 2.0],
 ['Australia', 'British Virgin Islands', 164.5],
 ['Australia', 'Cayman Islands', 13.0],
 ['Australia', 'Cook Islands', 3.0],
 ['Australia', 'Hong Kong', 1.0],
 ['Australia', 'Isle of Man', 6.0],
 ['Australia', 'Labuan', 0.5],
 ['Australia', 'Niue', 8.0],
 ['Australia', 'Panama', 40.0],
 ['Australia', 'Saint Kitts and Nevis', 4.0],
 ['Australia', 'Samoa', 0.5],
 ['Australia', 'Seychelles', 8.0],
 ['Austria', 'Bahamas', 8.0],
 ['Austria', 'Bermuda', 8.0],
 ['Austria', 'British Anguilla', 2.0],
 ['Austria', 'British Virgin Islands', 15.5],
 ['Austria', 'Cayman Islands', 4.0],
 ['Austria', 'Panama', 24.0],
 ['Austria', 'Saint Kitts and Nevis', 4.0],
 ['Austria', 'Samoa', 0.5],
 ['Azerbaijan', 'Bahamas', 8.0],
 ['Azerbaijan', 'British Virgin Islands', 8.0],
 ['Azerbaijan', 'Saint Kitts and Nevis', 4.0],
 ['Bahamas', 'Barbados', 100.0],
 ['Bahamas', 'Bermuda', 18.0],
 ['Bahamas', 'British Anguilla', 90.0],
 ['Bahamas', 'British Virgin Islands', 3530.5],
 ['Bahamas', 'Cayman Islands', 46.0],
 ['Bahamas', 'Cook Islands', 2.5],
 ['Bahamas', 'Costa Rica', 18.0],
 ['Bahamas', 'Cyprus', 4.0],
 ['Bahamas', 'Hong Kong', 12.0],
 ['Bahamas', 'Malta', 2.0],
 ['Bahamas', 'Netherlands Antilles', 2.0],
 ['Bahamas', 'Nevada', 582.0],
 ['Bahamas', 'Niue', 116.0],
 ['Bahamas', 'Panama', 4512.0],
 ['Bahamas', 'Saint Lucia', 0.5],
 ['Bahamas', 'Samoa', 54.0],
 ['Bahamas', 'Seychelles', 210.0],
 ['Bahamas', 'United Kingdom', 8.0],
 ['Bahamas', 'Uruguay', 4.0],
 ['Bahamas', 'Wyoming', 30.0],
 ['Bahrain', 'Bermuda', 6.0],
 ['Bahrain', 'British Virgin Islands', 20.0],
 ['Bahrain', 'Panama', 6.0],
 ['Bahrain', 'United Arab Emirates', 2.0],
 ['Bangladesh', 'Bermuda', 2.0],
 ['Bangladesh', 'British Virgin Islands', 4.0],
 ['Barbados', 'Bahamas', 4.0],
 ['Barbados', 'Bermuda', 16.0],
 ['Barbados', 'British Virgin Islands', 30.0],
 ['Barbados', 'Cayman Islands', 8.0],
 ['Barbados', 'Panama', 36.0],
 ['Belarus', 'British Virgin Islands', 28.5],
 ['Belarus', 'Hong Kong', 2.0],
 ['Belarus', 'Panama', 2.0],
 ['Belgium', 'Bahamas', 16.0],
 ['Belgium', 'Barbados', 2.0],
 ['Belgium', 'Bermuda', 2.0],
 ['Belgium', 'British Anguilla', 2.0],
 ['Belgium', 'British Virgin Islands', 38.0],
 ['Belgium', 'Isle of Man', 2.0],
 ['Belgium', 'Niue', 2.0],
 ['Belgium', 'Panama', 50.0],
 ['Belgium', 'Saint Kitts and Nevis', 2.0],
 ['Belgium', 'Samoa', 2.0],
 ['Belgium', 'Seychelles', 10.0],
 ['Belize', 'Bahamas', 500.0],
 ['Belize', 'Barbados', 6.0],
 ['Belize', 'British Anguilla', 38.0],
 ['Belize', 'British Virgin Islands', 332.5],
 ['Belize', 'Cayman Islands', 2.0],
 ['Belize', 'Hong Kong', 88.0],
 ['Belize', 'Mauritius', 2.0],
 ['Belize', 'Niue', 456.0],
 ['Belize', 'Panama', 846.0],
 ['Belize', 'Saint Kitts and Nevis', 2.0],
 ['Belize', 'Samoa', 90.0],
 ['Belize', 'Seychelles', 350.0],
 ['Belize', 'Wyoming', 2.0],
 ['Bermuda', 'Bahamas', 42.0],
 ['Bermuda', 'Barbados', 74.0],
 ['Bermuda', 'British Virgin Islands', 276.5],
 ['Bermuda', 'Cayman Islands', 34.0],
 ['Bermuda', 'Cook Islands', 2.0],
 ['Bermuda', 'Cyprus', 2.0],
 ['Bermuda', 'Jersey', 6.0],
 ['Bermuda', 'Liberia', 8.0],
 ['Bermuda', 'Liechtenstein', 10.0],
 ['Bermuda', 'Netherlands', 10.0],
 ['Bermuda', 'Netherlands Antilles', 2.0],
 ['Bermuda', 'Panama', 42.0],
 ['Bermuda', 'Saint Kitts and Nevis', 2.0],
 ['Bermuda', 'Samoa', 0.5],
 ['Bermuda', 'Singapore', 2.0],
 ['Bermuda', 'Turks and Caicos Islands', 2.0],
 ['Bermuda', 'United States', 42.0],
 ['Bolivia', 'British Virgin Islands', 4.0],
 ['Bolivia', 'Hong Kong', 4.0],
 ['Bolivia', 'Panama', 182.0],
 ['Botswana', 'British Virgin Islands', 4.0],
 ['Botswana', 'Panama', 2.0],
 ['Botswana', 'Seychelles', 6.0],
 ['Brazil', 'Bahamas', 66.0],
 ['Brazil', 'Barbados', 4.0],
 ['Brazil', 'Bermuda', 8.0],
 ['Brazil', 'British Anguilla', 28.0],
 ['Brazil', 'British Virgin Islands', 946.5],
 ['Brazil', 'Cayman Islands', 42.0],
 ['Brazil', 'Hong Kong', 4.0],
 ['Brazil', 'Isle of Man', 6.0],
 ['Brazil', 'Nevada', 264.0],
 ['Brazil', 'New Zealand', 2.0],
 ['Brazil', 'Niue', 182.0],
 ['Brazil', 'Panama', 1042.0],
 ['Brazil', 'Samoa', 86.0],
 ['Brazil', 'Seychelles', 158.0],
 ['Brazil', 'United Kingdom', 12.0],
 ['Brazil', 'Uruguay', 2.0],
 ['Brazil', 'Wyoming', 8.0],
 ['British Anguilla', 'Barbados', 14.0],
 ['British Anguilla', 'British Anguilla', 42.0],
 ['British Anguilla', 'British Virgin Islands', 4.0],
 ['British Virgin Islands', 'Bahamas', 290.0],
 ['British Virgin Islands', 'Barbados', 2106.0],
 ['British Virgin Islands', 'Belize', 2.0],
 ['British Virgin Islands', 'Bermuda', 26.0],
 ['British Virgin Islands', 'British Anguilla', 2.0],
 ['British Virgin Islands', 'Cayman Islands', 18.0],
 ['British Virgin Islands', 'Cook Islands', 151.0],
 ['British Virgin Islands', 'Hong Kong', 6.0],
 ['British Virgin Islands', 'Isle of Man', 4.0],
 ['British Virgin Islands', 'Labuan', 0.5],
 ['British Virgin Islands', 'Marshall Islands', 0.5],
 ['British Virgin Islands', 'Nevada', 4.0],
 ['British Virgin Islands', 'Niue', 4.0],
 ['British Virgin Islands', 'Panama', 184.0],
 ['British Virgin Islands', 'Saint Kitts and Nevis', 4.0],
 ['British Virgin Islands', 'Samoa', 1.5],
 ['British Virgin Islands', 'Seychelles', 22.5],
 ['British Virgin Islands', 'Singapore', 0.5],
 ['British Virgin Islands', 'State of Delaware', 2.0],
 ['Brunei', 'British Virgin Islands', 6.5],
 ['Bulgaria', 'Bahamas', 4.0],
 ['Bulgaria', 'British Virgin Islands', 40.0],
 ['Bulgaria', 'Niue', 2.0],
 ['Bulgaria', 'Panama', 10.0],
 ['Bulgaria', 'Seychelles', 44.0],
 ['Cameroon', 'Seychelles', 2.0],
 ['Canada', 'Bahamas', 72.0],
 ['Canada', 'Barbados', 264.0],
 ['Canada', 'Bermuda', 158.0],
 ['Canada', 'British Anguilla', 4.0],
 ['Canada', 'British Virgin Islands', 417.5],
 ['Canada', 'Cayman Islands', 128.5],
 ['Canada', 'Cook Islands', 4.5],
 ['Canada', 'Hong Kong', 3.0],
 ['Canada', 'Isle of Man', 10.0],
 ['Canada', 'Labuan', 1.0],
 ['Canada', 'Nevada', 4.0],
 ['Canada', 'Niue', 62.0],
 ['Canada', 'Panama', 148.0],
 ['Canada', 'Saint Kitts and Nevis', 2.0],
 ['Canada', 'Samoa', 4.0],
 ['Canada', 'Seychelles', 34.0],
 ['Canada', 'Singapore', 4.0],
 ['Cayman Islands', 'Antigua and Barbuda', 6.0],
 ['Cayman Islands', 'Aruba', 4.0],
 ['Cayman Islands', 'Bahamas', 86.0],
 ['Cayman Islands', 'Barbados', 226.0],
 ['Cayman Islands', 'Bermuda', 42.0],
 ['Cayman Islands', 'British Virgin Islands', 2115.0],
 ['Cayman Islands', 'Cook Islands', 72.5],
 ['Cayman Islands', 'Cyprus', 6.0],
 ['Cayman Islands', 'Gibraltar', 2.0],
 ['Cayman Islands', 'Grenada', 2.0],
 ['Cayman Islands', 'Guernsey', 18.0],
 ['Cayman Islands', 'Hong Kong', 152.0],
 ['Cayman Islands', 'Isle of Man', 16.0],
 ['Cayman Islands', 'Jersey', 4.0],
 ['Cayman Islands', 'Liberia', 2.0],
 ['Cayman Islands', 'Liechtenstein', 2.0],
 ['Cayman Islands', 'Luxembourg', 2.0],
 ['Cayman Islands', 'Malta', 2.0],
 ['Cayman Islands', 'Marshall Islands', 2.0],
 ['Cayman Islands', 'Netherlands', 2.0],
 ['Cayman Islands', 'Netherlands Antilles', 4.0],
 ['Cayman Islands', 'New Zealand', 4.0],
 ['Cayman Islands', 'Niue', 2.0],
 ['Cayman Islands', 'Panama', 106.0],
 ['Cayman Islands', 'Saint Kitts and Nevis', 192.0],
 ['Cayman Islands', 'Saint Vincent and the Grenadines', 24.0],
 ['Cayman Islands', 'Samoa', 1707.0],
 ['Cayman Islands', 'Singapore', 9.0],
 ['Cayman Islands', 'State of Delaware', 232.0],
 ['Cayman Islands', 'Turks and Caicos Islands', 2.0],
 ['Cayman Islands', 'US Virgin Islands', 2.0],
 ['Cayman Islands', 'United States', 44.0],
 ['Central African Republic', 'British Virgin Islands', 2.0],
 ['Chad', 'British Virgin Islands', 20.0],
 ['Chad', 'Panama', 2.0],
 ['Chile', 'Bahamas', 28.0],
 ['Chile', 'Barbados', 2.0],
 ['Chile', 'Bermuda', 2.0],
 ['Chile', 'British Anguilla', 4.0],
 ['Chile', 'British Virgin Islands', 62.0],
 ['Chile', 'Costa Rica', 2.0],
 ['Chile', 'Nevada', 12.0],
 ['Chile', 'Panama', 166.0],
 ['Chile', 'Seychelles', 4.0],
 ['Chile', 'Wyoming', 2.0],
 ['China', 'Bahamas', 10.0],
 ['China', 'Barbados', 2.0],
 ['China', 'Belize', 2.0],
 ['China', 'Bermuda', 154.0],
 ['China', 'British Anguilla', 296.0],
 ['China', 'British Virgin Islands', 4627.0],
 ['China', 'Cayman Islands', 294.5],
 ['China', 'Cook Islands', 3.0],
 ['China', 'Hong Kong', 19.0],
 ['China', 'Isle of Man', 2.0],
 ['China', 'Labuan', 0.5],
 ['China', 'Nevada', 18.0],
 ['China', 'Niue', 24.0],
 ['China', 'Panama', 26.0],
 ['China', 'Saint Kitts and Nevis', 4.0],
 ['China', 'Samoa', 733.5],
 ['China', 'Seychelles', 1251.0],
 ['China', 'Singapore', 1.5],
 ['Colombia', 'Bahamas', 44.0],
 ['Colombia', 'Belize', 38.0],
 ['Colombia', 'Bermuda', 4.0],
 ['Colombia', 'British Anguilla', 118.0],
 ['Colombia', 'British Virgin Islands', 530.0],
 ['Colombia', 'Cayman Islands', 6.0],
 ['Colombia', 'Costa Rica', 84.0],
 ['Colombia', 'Cyprus', 2.0],
 ['Colombia', 'Hong Kong', 2.0],
 ['Colombia', 'Malta', 2.0],
 ['Colombia', 'Nevada', 20.0],
 ['Colombia', 'New Zealand', 6.0],
 ['Colombia', 'Niue', 4.0],
 ['Colombia', 'Panama', 2714.0],
 ['Colombia', 'Saint Kitts and Nevis', 6.0],
 ['Colombia', 'Samoa', 12.0],
 ['Colombia', 'Seychelles', 36.0],
 ['Colombia', 'United Kingdom', 102.0],
 ['Colombia', 'Uruguay', 2.0],
 ['Cook Islands', 'Bermuda', 2.0],
 ['Cook Islands', 'British Virgin Islands', 11.5],
 ['Cook Islands', 'Hong Kong', 2.0],
 ['Cook Islands', 'Niue', 2.0],
 ['Cook Islands', 'Samoa', 2.0],
 ['Costa Rica', 'Bahamas', 64.0],
 ['Costa Rica', 'British Anguilla', 22.0],
 ['Costa Rica', 'British Virgin Islands', 282.0],
 ['Costa Rica', 'Cayman Islands', 4.0],
 ['Costa Rica', 'Hong Kong', 10.0],
 ['Costa Rica', 'Isle of Man', 2.0],
 ['Costa Rica', 'Nevada', 4.0],
 ['Costa Rica', 'Niue', 8.0],
 ['Costa Rica', 'Panama', 1372.0],
 ['Costa Rica', 'Samoa', 4.5],
 ['Costa Rica', 'Seychelles', 4.0],
 ['Costa Rica', 'United Kingdom', 2.0],
 ['Croatia', 'Barbados', 2.0],
 ['Croatia', 'British Virgin Islands', 4.5],
 ['Croatia', 'Panama', 2.0],
 ['Cuba', 'Barbados', 2.0],
 ['Cuba', 'British Virgin Islands', 16.0],
 ['Cuba', 'Cayman Islands', 2.0],
 ['Cuba', 'Panama', 6.0],
 ['Curaçao', 'Barbados', 38.0],
 ['Curaçao', 'Bermuda', 2.0],
 ['Curaçao', 'British Virgin Islands', 20.5],
 ['Curaçao', 'Panama', 16.0],
 ['Curaçao', 'Seychelles', 24.0],
 ['Curaçao', 'Singapore', 0.5],
 ['Cyprus', 'Bahamas', 1298.0],
 ['Cyprus', 'Barbados', 10.0],
 ['Cyprus', 'Belize', 22.0],
 ['Cyprus', 'Bermuda', 16.0],
 ['Cyprus', 'British Anguilla', 10.0],
 ['Cyprus', 'British Virgin Islands', 3750.5],
 ['Cyprus', 'Cayman Islands', 20.0],
 ['Cyprus', 'Cook Islands', 2.0],
 ['Cyprus', 'Hong Kong', 8.0],
 ['Cyprus', 'Isle of Man', 8.0],
 ['Cyprus', 'Labuan', 1.0],
 ['Cyprus', 'Mauritius', 2.0],
 ['Cyprus', 'Nevada', 2.0],
 ['Cyprus', 'Niue', 626.0],
 ['Cyprus', 'Panama', 610.0],
 ['Cyprus', 'Saint Kitts and Nevis', 6.0],
 ['Cyprus', 'Saint Lucia', 0.5],
 ['Cyprus', 'Samoa', 14.5],
 ['Cyprus', 'Seychelles', 896.0],
 ['Cyprus', 'Singapore', 1.0],
 ['Czech Republic', 'Bahamas', 4.0],
 ['Czech Republic', 'Barbados', 2.0],
 ['Czech Republic', 'British Virgin Islands', 52.0],
 ['Czech Republic', 'Isle of Man', 2.0],
 ['Czech Republic', 'Panama', 12.0],
 ['Czech Republic', 'Samoa', 2.0],
 ['Czech Republic', 'Seychelles', 2.0],
 ["Côte d'Ivoire", 'British Virgin Islands', 20.0],
 ["Côte d'Ivoire", 'Nevada', 2.0],
 ["Côte d'Ivoire", 'Niue', 2.0],
 ["Côte d'Ivoire", 'Panama', 14.0],
 ["Côte d'Ivoire", 'Samoa', 4.0],
 ['Denmark', 'Bahamas', 8.0],
 ['Denmark', 'Barbados', 2.0],
 ['Denmark', 'Bermuda', 4.0],
 ['Denmark', 'British Virgin Islands', 8.0],
 ['Denmark', 'Panama', 8.0],
 ['Denmark', 'Seychelles', 4.0],
 ['Djibouti', 'Panama', 2.0],
 ['Dominica', 'Barbados', 2.0],
 ['Dominica', 'British Virgin Islands', 8.0],
 ['Dominica', 'Panama', 124.0],
 ['Dominican Republic', 'Bahamas', 16.0],
 ['Dominican Republic', 'Barbados', 6.0],
 ['Dominican Republic', 'British Anguilla', 48.0],
 ['Dominican Republic', 'British Virgin Islands', 264.0],
 ['Dominican Republic', 'Cayman Islands', 10.0],
 ['Dominican Republic', 'Panama', 624.0],
 ['Dominican Republic', 'Samoa', 2.0],
 ['Dominican Republic', 'Seychelles', 22.0],
 ['Ecuador', 'Bahamas', 98.0],
 ['Ecuador', 'Barbados', 4.0],
 ['Ecuador', 'Belize', 10.0],
 ['Ecuador', 'Bermuda', 2.0],
 ['Ecuador', 'British Anguilla', 292.0],
 ['Ecuador', 'British Virgin Islands', 504.0],
 ['Ecuador', 'Cayman Islands', 2.0],
 ['Ecuador', 'Costa Rica', 16.0],
 ['Ecuador', 'Hong Kong', 4.0],
 ['Ecuador', 'Nevada', 398.0],
 ['Ecuador', 'New Zealand', 18.0],
 ['Ecuador', 'Niue', 52.0],
 ['Ecuador', 'Panama', 2202.0],
 ['Ecuador', 'Samoa', 40.0],
 ['Ecuador', 'Seychelles', 52.0],
 ['Ecuador', 'United Kingdom', 14.0],
 ['Ecuador', 'Uruguay', 4.0],
 ['Egypt', 'Bahamas', 10.0],
 ['Egypt', 'Barbados', 2.0],
 ['Egypt', 'Bermuda', 6.0],
 ['Egypt', 'British Virgin Islands', 34.0],
 ['Egypt', 'Cayman Islands', 2.0],
 ['Egypt', 'Panama', 40.0],
 ['Egypt', 'Seychelles', 2.0],
 ['El Salvador', 'Bahamas', 12.0],
 ['El Salvador', 'Barbados', 4.0],
 ['El Salvador', 'British Anguilla', 2.0],
 ['El Salvador', 'British Virgin Islands', 38.0],
 ['El Salvador', 'Nevada', 14.0],
 ['El Salvador', 'Niue', 2.0],
 ['El Salvador', 'Panama', 390.0],
 ['El Salvador', 'Seychelles', 10.0],
 ['Estonia', 'Bahamas', 228.0],
 ['Estonia', 'British Anguilla', 2.0],
 ['Estonia', 'British Virgin Islands', 1288.5],
 ['Estonia', 'Cyprus', 2.0],
 ['Estonia', 'Isle of Man', 2.0],
 ['Estonia', 'Malta', 2.0],
 ['Estonia', 'Niue', 136.0],
 ['Estonia', 'Panama', 28.0],
 ['Estonia', 'Samoa', 6.0],
 ['Estonia', 'Seychelles', 68.0],
 ['Fiji', 'British Virgin Islands', 1.0],
 ['Fiji', 'Cook Islands', 2.0],
 ['Fiji', 'Singapore', 0.5],
 ['Finland', 'Bahamas', 64.0],
 ['Finland', 'British Virgin Islands', 50.5],
 ['Finland', 'Cayman Islands', 14.0],
 ['Finland', 'Niue', 2.0],
 ['Finland', 'Panama', 6.0],
 ['Finland', 'Seychelles', 8.0],
 ['France', 'Bahamas', 46.0],
 ['France', 'Barbados', 4.0],
 ['France', 'Bermuda', 50.0],
 ['France', 'British Anguilla', 20.0],
 ['France', 'British Virgin Islands', 279.5],
 ['France', 'Cayman Islands', 12.0],
 ['France', 'Cook Islands', 3.0],
 ['France', 'Cyprus', 0.5],
 ['France', 'Hong Kong', 4.0],
 ['France', 'Isle of Man', 2.0],
 ['France', 'Labuan', 0.5],
 ['France', 'Nevada', 2.0],
 ['France', 'Niue', 36.0],
 ['France', 'Panama', 156.0],
 ['France', 'Samoa', 6.5],
 ['France', 'Seychelles', 28.0],
 ['French Guiana', 'British Virgin Islands', 40.0],
 ['French Guiana', 'Cayman Islands', 16.0],
 ['French Guiana', 'Guernsey', 4.0],
 ['Georgia', 'British Anguilla', 2.0],
 ['Georgia', 'Cook Islands', 0.5],
 ['Germany', 'Bahamas', 2.0],
 ['Germany', 'Barbados', 6.0],
 ['Germany', 'Bermuda', 8.0],
 ['Germany', 'British Anguilla', 2.0],
 ['Germany', 'British Virgin Islands', 71.5],
 ['Germany', 'Cayman Islands', 10.0],
 ['Germany', 'Cyprus', 2.0],
 ['Germany', 'Isle of Man', 2.0],
 ['Germany', 'Niue', 4.0],
 ['Germany', 'Panama', 258.0],
 ['Germany', 'Samoa', 0.5],
 ['Germany', 'Seychelles', 16.0],
 ['Ghana', 'Bahamas', 2.0],
 ['Ghana', 'British Anguilla', 2.0],
 ['Ghana', 'British Virgin Islands', 2.0],
 ['Gibraltar', 'Bahamas', 300.0],
 ['Gibraltar', 'Barbados', 16.0],
 ['Gibraltar', 'Belize', 4.0],
 ['Gibraltar', 'British Virgin Islands', 3168.5],
 ['Gibraltar', 'Cayman Islands', 2.0],
 ['Gibraltar', 'Isle of Man', 8.0],
 ['Gibraltar', 'Malta', 2.0],
 ['Gibraltar', 'Nevada', 4.0],
 ['Gibraltar', 'Niue', 56.0],
 ['Gibraltar', 'Panama', 390.0],
 ['Gibraltar', 'Samoa', 28.0],
 ['Gibraltar', 'Seychelles', 128.0],
 ['Gibraltar', 'Singapore', 0.5],
 ['Greece', 'Bahamas', 14.0],
 ['Greece', 'Bermuda', 10.0],
 ['Greece', 'British Virgin Islands', 125.0],
 ['Greece', 'Cayman Islands', 2.0],
 ['Greece', 'Liberia', 2.0],
 ['Greece', 'Panama', 268.0],
 ['Greece', 'Seychelles', 42.0],
 ['Grenada', 'Barbados', 4.0],
 ['Grenada', 'British Virgin Islands', 1.0],
 ['Grenada', 'Saint Kitts and Nevis', 8.0],
 ['Guam', 'British Virgin Islands', 2.0],
 ['Guatemala', 'Bahamas', 78.0],
 ['Guatemala', 'British Anguilla', 46.0],
 ['Guatemala', 'British Virgin Islands', 370.0],
 ['Guatemala', 'Cayman Islands', 16.0],
 ['Guatemala', 'Niue', 280.0],
 ['Guatemala', 'Panama', 1602.0],
 ['Guatemala', 'Saint Kitts and Nevis', 8.0],
 ['Guatemala', 'Samoa', 32.0],
 ['Guatemala', 'Seychelles', 60.0],
 ['Guernsey', 'Bahamas', 3008.0],
 ['Guernsey', 'Barbados', 26.0],
 ['Guernsey', 'Bermuda', 6.0],
 ['Guernsey', 'British Anguilla', 54.0],
 ['Guernsey', 'British Virgin Islands', 9469.0],
 ['Guernsey', 'Cayman Islands', 8.0],
 ['Guernsey', 'Cook Islands', 4.0],
 ['Guernsey', 'Cyprus', 2.0],
 ['Guernsey', 'Hong Kong', 4.0],
 ['Guernsey', 'Isle of Man', 44.0],
 ['Guernsey', 'Jersey', 16.0],
 ['Guernsey', 'Niue', 604.0],
 ['Guernsey', 'Panama', 1410.0],
 ['Guernsey', 'Samoa', 38.0],
 ['Guernsey', 'Seychelles', 104.0],
 ['Guernsey', 'Turks and Caicos Islands', 2.0],
 ['Guernsey', 'Uruguay', 2.0],
 ['Guyana', 'Barbados', 4.0],
 ['Haiti', 'British Virgin Islands', 6.0],
 ['Haiti', 'Nevada', 10.0],
 ['Honduras', 'Bahamas', 2.0],
 ['Honduras', 'British Virgin Islands', 8.0],
 ['Honduras', 'Cayman Islands', 2.0],
 ['Honduras', 'Panama', 38.0],
 ['Hong Kong', 'Bahamas', 1921.0],
 ['Hong Kong', 'Barbados', 10.0],
 ['Hong Kong', 'Belize', 12.0],
 ['Hong Kong', 'Bermuda', 786.0],
 ['Hong Kong', 'British Anguilla', 2732.0],
 ['Hong Kong', 'British Virgin Islands', 55250.5],
 ['Hong Kong', 'Cayman Islands', 460.5],
 ['Hong Kong', 'Cook Islands', 34.0],
 ['Hong Kong', 'Isle of Man', 20.0],
 ['Hong Kong', 'Labuan', 12.5],
 ['Hong Kong', 'Marshall Islands', 0.5],
 ['Hong Kong', 'Mauritius', 2.5],
 ['Hong Kong', 'Netherlands', 2.0],
 ['Hong Kong', 'Nevada', 86.0],
 ['Hong Kong', 'Niue', 2110.0],
 ['Hong Kong', 'Panama', 1218.5],
 ['Hong Kong', 'Samoa', 6044.0],
 ['Hong Kong', 'Seychelles', 7500.5],
 ['Hong Kong', 'Singapore', 21.5],
 ['Hong Kong', 'Thailand', 2.0],
 ['Hong Kong', 'Vanuatu', 0.5],
 ['Hungary', 'Bahamas', 8.0],
 ['Hungary', 'Barbados', 6.0],
 ['Hungary', 'British Virgin Islands', 50.0],
 ['Hungary', 'Nevada', 2.0],
 ['Hungary', 'Niue', 50.0],
 ['Hungary', 'Panama', 14.0],
 ['Hungary', 'Samoa', 2.0],
 ['Hungary', 'Seychelles', 54.0],
 ['Iceland', 'Bermuda', 4.0],
 ['Iceland', 'British Virgin Islands', 28.0],
 ['Iceland', 'Panama', 2.0],
 ['India', 'Barbados', 2.0],
 ['India', 'Bermuda', 20.0],
 ['India', 'British Virgin Islands', 32.5],
 ['India', 'Cayman Islands', 4.0],
 ['India', 'Isle of Man', 16.0],
 ['India', 'Niue', 2.0],
 ['India', 'Panama', 4.0],
 ['India', 'Samoa', 1.0],
 ['India', 'Seychelles', 12.0],
 ['India', 'Singapore', 2.0],
 ['Indonesia', 'Bahamas', 0.5],
 ['Indonesia', 'Bermuda', 10.0],
 ['Indonesia', 'British Virgin Islands', 149.0],
 ['Indonesia', 'Cayman Islands', 20.0],
 ['Indonesia', 'Cook Islands', 13.0],
 ['Indonesia', 'Hong Kong', 1.5],
 ['Indonesia', 'Panama', 2.0],
 ['Indonesia', 'Samoa', 10.5],
 ['Indonesia', 'Seychelles', 5.0],
 ['Indonesia', 'Singapore', 10.5],
 ['Iran', 'Panama', 8.0],
 ['Ireland', 'Bahamas', 50.0],
 ['Ireland', 'Barbados', 26.0],
 ['Ireland', 'Bermuda', 40.0],
 ['Ireland', 'British Anguilla', 2.0],
 ['Ireland', 'British Virgin Islands', 374.0],
 ['Ireland', 'Cayman Islands', 18.0],
 ['Ireland', 'Guernsey', 4.0],
 ['Ireland', 'Hong Kong', 2.0],
 ['Ireland', 'Isle of Man', 60.0],
 ['Ireland', 'Jersey', 2.0],
 ['Ireland', 'Niue', 62.0],
 ['Ireland', 'Panama', 112.0],
 ['Ireland', 'Samoa', 22.0],
 ['Ireland', 'Seychelles', 26.0],
 ['Isle of Man', 'Bahamas', 1770.0],
 ['Isle of Man', 'Barbados', 52.0],
 ['Isle of Man', 'Bermuda', 10.0],
 ['Isle of Man', 'British Anguilla', 70.0],
 ['Isle of Man', 'British Virgin Islands', 6128.5],
 ['Isle of Man', 'Cayman Islands', 24.0],
 ['Isle of Man', 'Cyprus', 14.0],
 ['Isle of Man', 'Guernsey', 8.0],
 ['Isle of Man', 'Hong Kong', 14.0],
 ['Isle of Man', 'Isle of Man', 14.0],
 ['Isle of Man', 'Jersey', 18.0],
 ['Isle of Man', 'Malta', 2.0],
 ['Isle of Man', 'Nevada', 14.0],
 ['Isle of Man', 'Niue', 542.0],
 ['Isle of Man', 'Panama', 1000.0],
 ['Isle of Man', 'Samoa', 140.0],
 ['Isle of Man', 'Seychelles', 160.0],
 ['Isle of Man', 'United Arab Emirates', 2.0],
 ['Isle of Man', 'United Kingdom', 4.0],
 ['Isle of Man', 'Uruguay', 2.0],
 ['Israel', 'Bahamas', 32.0],
 ['Israel', 'Barbados', 2.0],
 ['Israel', 'Belize', 20.0],
 ['Israel', 'Bermuda', 2.0],
 ['Israel', 'British Anguilla', 300.0],
 ['Israel', 'British Virgin Islands', 350.5],
 ['Israel', 'Cayman Islands', 8.0],
 ['Israel', 'Cyprus', 8.0],
 ['Israel', 'Hong Kong', 2.0],
 ['Israel', 'Isle of Man', 4.0],
 ['Israel', 'Malta', 8.0],
 ['Israel', 'Niue', 22.0],
 ['Israel', 'Panama', 254.0],
 ['Israel', 'Samoa', 94.0],
 ['Israel', 'Seychelles', 244.0],
 ['Italy', 'Bahamas', 6.0],
 ['Italy', 'British Anguilla', 408.0],
 ['Italy', 'British Virgin Islands', 63.0],
 ['Italy', 'Cayman Islands', 4.0],
 ['Italy', 'Cook Islands', 0.5],
 ['Italy', 'Cyprus', 26.0],
 ['Italy', 'Hong Kong', 2.0],
 ['Italy', 'Isle of Man', 4.0],
 ['Italy', 'Nevada', 14.0],
 ['Italy', 'Niue', 44.0],
 ['Italy', 'Panama', 80.0],
 ['Italy', 'Samoa', 6.0],
 ['Italy', 'Seychelles', 44.0],
 ['Jamaica', 'Bahamas', 2.0],
 ['Jamaica', 'Barbados', 24.0],
 ['Jamaica', 'British Virgin Islands', 8.0],
 ['Jamaica', 'Cayman Islands', 10.0],
 ['Japan', 'Bermuda', 16.0],
 ['Japan', 'British Virgin Islands', 34.0],
 ['Japan', 'Cayman Islands', 32.0],
 ['Japan', 'Cook Islands', 2.0],
 ['Japan', 'Niue', 4.0],
 ['Japan', 'Panama', 14.0],
 ['Japan', 'Samoa', 0.5],
 ['Japan', 'Seychelles', 2.0],
 ['Japan', 'Singapore', 2.0],
 ['Jersey', 'Bahamas', 2488.0],
 ['Jersey', 'Barbados', 88.0],
 ['Jersey', 'Bermuda', 26.0],
 ['Jersey', 'British Anguilla', 4.0],
 ['Jersey', 'British Virgin Islands', 21208.5],
 ['Jersey', 'Cayman Islands', 20.0],
 ['Jersey', 'Cook Islands', 4.0],
 ['Jersey', 'Cyprus', 2.0],
 ['Jersey', 'Hong Kong', 132.0],
 ['Jersey', 'Isle of Man', 38.0],
 ['Jersey', 'Malta', 8.0],
 ['Jersey', 'Mauritius', 2.0],
 ['Jersey', 'Nevada', 6.0],
 ['Jersey', 'Niue', 688.0],
 ['Jersey', 'Panama', 4258.0],
 ['Jersey', 'Saint Kitts and Nevis', 2.0],
 ['Jersey', 'Samoa', 35.0],
 ['Jersey', 'Seychelles', 36.0],
 ['Jersey', 'Singapore', 2.0],
 ['Jersey', 'United Kingdom', 12.0],
 ['Jersey', 'Wyoming', 2.0],
 ['Jordan', 'Bahamas', 28.0],
 ['Jordan', 'Bermuda', 2.0],
 ['Jordan', 'British Virgin Islands', 2292.0],
 ['Jordan', 'Panama', 788.0],
 ['Jordan', 'Seychelles', 2.0],
 ['Kazakhstan', 'Bermuda', 2.0],
 ['Kazakhstan', 'British Virgin Islands', 16.0],
 ['Kazakhstan', 'Cayman Islands', 0.5],
 ['Kazakhstan', 'Labuan', 0.5],
 ['Kenya', 'Bahamas', 2.0],
 ['Kenya', 'British Anguilla', 4.0],
 ['Kenya', 'British Virgin Islands', 30.0],
 ['Kenya', 'Cayman Islands', 2.0],
 ['Kenya', 'Isle of Man', 2.0],
 ['Kenya', 'Panama', 12.0],
 ['Kenya', 'Seychelles', 2.0],
 ['Kuwait', 'Bahamas', 4.0],
 ['Kuwait', 'Bermuda', 2.0],
 ['Kuwait', 'British Virgin Islands', 18.0],
 ['Kuwait', 'Cayman Islands', 8.0],
 ['Kuwait', 'Panama', 72.0],
 ['Kuwait', 'Seychelles', 26.0],
 ['Kyrgyzstan', 'Isle of Man', 2.0],
 ['Latvia', 'Bahamas', 102.0],
 ['Latvia', 'British Virgin Islands', 2607.0],
 ['Latvia', 'Cook Islands', 0.5],
 ['Latvia', 'Hong Kong', 2.0],
 ['Latvia', 'Niue', 26.0],
 ['Latvia', 'Panama', 24.0],
 ['Latvia', 'Seychelles', 2.0],
 ['Lebanon', 'Bahamas', 14.0],
 ['Lebanon', 'Bermuda', 2.0],
 ['Lebanon', 'British Virgin Islands', 426.0],
 ['Lebanon', 'Cayman Islands', 2.0],
 ['Lebanon', 'Hong Kong', 6.0],
 ['Lebanon', 'Niue', 92.0],
 ['Lebanon', 'Panama', 242.0],
 ['Lebanon', 'Samoa', 24.0],
 ['Lebanon', 'Seychelles', 176.0],
 ['Lesotho', 'Seychelles', 2.0],
 ['Liberia', 'Barbados', 38.0],
 ['Liberia', 'Bermuda', 8.0],
 ['Liberia', 'Liechtenstein', 72.0],
 ['Liberia', 'Panama', 4.0],
 ['Libya', 'British Virgin Islands', 2.0],
 ['Libya', 'Panama', 2.0],
 ['Liechtenstein', 'Bahamas', 174.0],
 ['Liechtenstein', 'Barbados', 10.0],
 ['Liechtenstein', 'Bermuda', 8.0],
 ['Liechtenstein', 'British Anguilla', 10.0],
 ['Liechtenstein', 'British Virgin Islands', 2537.0],
 ['Liechtenstein', 'Cayman Islands', 7.0],
 ['Liechtenstein', 'Cyprus', 2.0],
 ['Liechtenstein', 'Hong Kong', 22.0],
 ['Liechtenstein', 'Niue', 98.0],
 ['Liechtenstein', 'Panama', 888.0],
 ['Liechtenstein', 'Samoa', 64.0],
 ['Liechtenstein', 'Seychelles', 338.0],
 ['Liechtenstein', 'Singapore', 6.0],
 ['Lithuania', 'Bahamas', 4.0],
 ['Lithuania', 'British Anguilla', 2.0],
 ['Lithuania', 'British Virgin Islands', 8.0],
 ['Lithuania', 'Niue', 40.0],
 ['Lithuania', 'Panama', 12.0],
 ['Luxembourg', 'Bahamas', 846.0],
 ['Luxembourg', 'Barbados', 22.0],
 ['Luxembourg', 'Belize', 16.0],
 ['Luxembourg', 'Bermuda', 8.0],
 ['Luxembourg', 'British Anguilla', 14.0],
 ['Luxembourg', 'British Virgin Islands', 6164.5],
 ['Luxembourg', 'Cayman Islands', 16.0],
 ['Luxembourg', 'Costa Rica', 2.0],
 ['Luxembourg', 'Cyprus', 12.0],
 ['Luxembourg', 'Hong Kong', 78.0],
 ['Luxembourg', 'Malta', 12.0],
 ['Luxembourg', 'Nevada', 58.0],
 ['Luxembourg', 'Niue', 438.0],
 ['Luxembourg', 'Panama', 11168.0],
 ['Luxembourg', 'Samoa', 26.0],
 ['Luxembourg', 'Seychelles', 2842.0],
 ['Luxembourg', 'United Kingdom', 2.0],
 ['Luxembourg', 'Uruguay', 2.0],
 ['Luxembourg', 'Wyoming', 2.0],
 ['Macao', 'Bermuda', 4.0],
 ['Macao', 'British Virgin Islands', 50.0],
 ['Macao', 'Cook Islands', 0.5],
 ['Macao', 'Malta', 230.0],
 ['Macao', 'Seychelles', 2.0],
 ['Macedonia', 'British Virgin Islands', 2.0],
 ['Macedonia', 'Seychelles', 2.0],
 ['Malawi', 'British Virgin Islands', 2.0],
 ['Malaysia', 'Bahamas', 2.0],
 ['Malaysia', 'Barbados', 2.0],
 ['Malaysia', 'Bermuda', 34.0],
 ['Malaysia', 'British Anguilla', 2.0],
 ['Malaysia', 'British Virgin Islands', 119.5],
 ['Malaysia', 'Cayman Islands', 2.5],
 ['Malaysia', 'Cook Islands', 2.5],
 ['Malaysia', 'Hong Kong', 1.5],
 ['Malaysia', 'Labuan', 845.0],
 ['Malaysia', 'Niue', 8.0],
 ['Malaysia', 'Panama', 4.0],
 ['Malaysia', 'Saint Lucia', 0.5],
 ['Malaysia', 'Samoa', 5.0],
 ['Malaysia', 'Seychelles', 2.0],
 ['Malaysia', 'Singapore', 5.0],
 ['Mali', 'British Virgin Islands', 16.0],
 ['Malta', 'Bahamas', 22.0],
 ['Malta', 'Barbados', 2.0],
 ['Malta', 'British Virgin Islands', 926.0],
 ['Malta', 'Hong Kong', 2.0],
 ['Malta', 'Isle of Man', 2.0],
 ['Malta', 'New Zealand', 18.0],
 ['Malta', 'Niue', 110.0],
 ['Malta', 'Panama', 214.0],
 ['Malta', 'Seychelles', 62.0],
 ['Marshall Islands', 'Barbados', 2.0],
 ['Marshall Islands', 'Cayman Islands', 2.0],
 ['Marshall Islands', 'Isle of Man', 2.0],
 ['Mauritius', 'Bahamas', 272.0],
 ['Mauritius', 'Barbados', 6.0],
 ['Mauritius', 'Bermuda', 4.0],
 ['Mauritius', 'British Virgin Islands', 1844.0],
 ['Mauritius', 'Cayman Islands', 4.0],
 ['Mauritius', 'Cook Islands', 0.5],
 ['Mauritius', 'Isle of Man', 2.0],
 ['Mauritius', 'Jersey', 6.0],
 ['Mauritius', 'Niue', 64.0],
 ['Mauritius', 'Panama', 216.0],
 ['Mauritius', 'Samoa', 19.5],
 ['Mauritius', 'Seychelles', 30.0],
 ['Mexico', 'Bahamas', 6.0],
 ['Mexico', 'Barbados', 6.0],
 ['Mexico', 'Bermuda', 2.0],
 ['Mexico', 'British Virgin Islands', 29.0],
 ['Mexico', 'Cayman Islands', 10.0],
 ['Mexico', 'Hong Kong', 2.0],
 ['Mexico', 'Nevada', 2.0],
 ['Mexico', 'Panama', 78.0],
 ['Mexico', 'Seychelles', 12.0],
 ['Mexico', 'United Kingdom', 2.0],
 ['Moldova', 'British Virgin Islands', 4.0],
 ['Monaco', 'Bahamas', 166.0],
 ['Monaco', 'Belize', 4.0],
 ['Monaco', 'Bermuda', 64.0],
 ['Monaco', 'British Anguilla', 8.0],
 ['Monaco', 'British Virgin Islands', 2079.0],
 ['Monaco', 'Cayman Islands', 16.0],
 ['Monaco', 'Hong Kong', 2.0],
 ['Monaco', 'Isle of Man', 4.0],
 ['Monaco', 'Nevada', 12.0],
 ['Monaco', 'Niue', 98.0],
 ['Monaco', 'Panama', 2804.0],
 ['Monaco', 'Samoa', 40.0],
 ['Monaco', 'Seychelles', 1132.0],
 ['Mongolia', 'British Virgin Islands', 6.0],
 ['Montenegro', 'British Virgin Islands', 24.0],
 ['Montenegro', 'Panama', 2.0],
 ['Morocco', 'British Virgin Islands', 42.0],
 ['Morocco', 'Panama', 4.0],
 ['Morocco', 'Seychelles', 36.0],
 ['Mozambique', 'British Virgin Islands', 2.0],
 ['Mozambique', 'Seychelles', 4.0],
 ['Namibia', 'Seychelles', 4.0],
 ['Nauru', 'Panama', 2.0],
 ['Netherlands', 'Bahamas', 56.0],
 ['Netherlands', 'Barbados', 36.0],
 ['Netherlands', 'Bermuda', 22.0],
 ['Netherlands', 'British Anguilla', 8.0],
 ['Netherlands', 'British Virgin Islands', 162.5],
 ['Netherlands', 'Cayman Islands', 12.0],
 ['Netherlands', 'Cook Islands', 0.5],
 ['Netherlands', 'Isle of Man', 4.0],
 ['Netherlands', 'Netherlands Antilles', 2.0],
 ['Netherlands', 'Niue', 14.0],
 ['Netherlands', 'Panama', 106.0],
 ['Netherlands', 'Samoa', 6.5],
 ['Netherlands', 'Seychelles', 12.0],
 ['Netherlands', 'Singapore', 3.0],
 ['New Zealand', 'Bahamas', 14.0],
 ['New Zealand', 'Bermuda', 36.0],
 ['New Zealand', 'British Virgin Islands', 43.5],
 ['New Zealand', 'Cook Islands', 3.5],
 ['New Zealand', 'Hong Kong', 2.0],
 ['New Zealand', 'Nevada', 2.0],
 ['New Zealand', 'Niue', 26.0],
 ['New Zealand', 'Panama', 20.0],
 ['New Zealand', 'Samoa', 8.0],
 ['Nicaragua', 'British Virgin Islands', 4.0],
 ['Nicaragua', 'Cayman Islands', 2.0],
 ['Nicaragua', 'Panama', 20.0],
 ['Nigeria', 'Bahamas', 10.0],
 ['Nigeria', 'Barbados', 2.0],
 ['Nigeria', 'Bermuda', 2.0],
 ['Nigeria', 'British Virgin Islands', 24.0],
 ['Nigeria', 'Cayman Islands', 4.0],
 ['Nigeria', 'Panama', 4.0],
 ['Nigeria', 'Seychelles', 4.0],
 ['Niue', 'Bahamas', 2.0],
 ['Niue', 'British Virgin Islands', 26.0],
 ['Niue', 'Panama', 472.0],
 ['Niue', 'Seychelles', 10.0],
 ['Northern Mariana Islands', 'British Virgin Islands', 0.5],
 ['Norway', 'Bahamas', 2.0],
 ['Norway', 'Barbados', 4.0],
 ['Norway', 'Bermuda', 52.0],
 ['Norway', 'British Virgin Islands', 11.5],
 ['Norway', 'Cayman Islands', 12.0],
 ['Norway', 'Panama', 16.0],
 ['Oman', 'British Virgin Islands', 6.0],
 ['Oman', 'Cayman Islands', 2.0],
 ['Pakistan', 'Bahamas', 2.0],
 ['Pakistan', 'Bermuda', 2.0],
 ['Pakistan', 'British Virgin Islands', 2.0],
 ['Pakistan', 'Panama', 2.0],
 ['Panama', 'Bahamas', 2240.0],
 ['Panama', 'Barbados', 50.0],
 ['Panama', 'Belize', 80.0],
 ['Panama', 'British Anguilla', 556.0],
 ['Panama', 'British Virgin Islands', 9577.5],
 ['Panama', 'Cayman Islands', 8.0],
 ['Panama', 'Cook Islands', 0.5],
 ['Panama', 'Costa Rica', 10.0],
 ['Panama', 'Cyprus', 44.0],
 ['Panama', 'Hong Kong', 216.0],
 ['Panama', 'Malta', 2.0],
 ['Panama', 'Nevada', 164.0],
 ['Panama', 'New Zealand', 4.0],
 ['Panama', 'Niue', 3178.0],
 ['Panama', 'Samoa', 820.0],
 ['Panama', 'Seychelles', 2546.0],
 ['Panama', 'United Kingdom', 10.0],
 ['Panama', 'Uruguay', 14.0],
 ['Panama', 'Wyoming', 10.0],
 ['Paraguay', 'Bahamas', 20.0],
 ['Paraguay', 'British Virgin Islands', 54.0],
 ['Paraguay', 'Nevada', 10.0],
 ['Paraguay', 'Niue', 2.0],
 ['Paraguay', 'Panama', 168.0],
 ['Peru', 'Bahamas', 4.0],
 ['Peru', 'Barbados', 2.0],
 ['Peru', 'Bermuda', 2.0],
 ['Peru', 'British Virgin Islands', 20.0],
 ['Peru', 'Cyprus', 2.0],
 ['Peru', 'Hong Kong', 2.0],
 ['Peru', 'Isle of Man', 2.0],
 ['Peru', 'New Zealand', 2.0],
 ['Peru', 'Niue', 2.0],
 ['Peru', 'Panama', 70.0],
 ['Peru', 'Seychelles', 2.0],
 ['Philippines', 'Bahamas', 4.0],
 ['Philippines', 'Bermuda', 18.0],
 ['Philippines', 'British Anguilla', 2.0],
 ['Philippines', 'British Virgin Islands', 42.5],
 ['Philippines', 'Cayman Islands', 4.0],
 ['Philippines', 'Hong Kong', 0.5],
 ['Philippines', 'Niue', 20.0],
 ['Philippines', 'Panama', 18.0],
 ['Philippines', 'Seychelles', 8.0],
 ['Poland', 'Bahamas', 116.0],
 ['Poland', 'British Virgin Islands', 57.0],
 ['Poland', 'Niue', 80.0],
 ['Poland', 'Panama', 8.0],
 ['Poland', 'Samoa', 2.0],
 ['Poland', 'Seychelles', 52.0],
 ['Poland', 'Wyoming', 2.0],
 ['Portugal', 'Bahamas', 88.0],
 ['Portugal', 'Barbados', 2.0],
 ['Portugal', 'Bermuda', 4.0],
 ['Portugal', 'British Virgin Islands', 125.0],
 ['Portugal', 'Cayman Islands', 2.0],
 ['Portugal', 'Cyprus', 10.0],
 ['Portugal', 'Hong Kong', 2.0],
 ['Portugal', 'Nevada', 4.0],
 ['Portugal', 'Niue', 112.0],
 ['Portugal', 'Panama', 126.0],
 ['Portugal', 'Samoa', 20.0],
 ['Puerto Rico', 'Barbados', 2.0],
 ['Puerto Rico', 'British Virgin Islands', 0.5],
 ['Puerto Rico', 'Panama', 2.0],
 ['Qatar', 'Bahamas', 8.0],
 ['Qatar', 'Bermuda', 2.0],
 ['Qatar', 'British Virgin Islands', 20.0],
 ['Qatar', 'Isle of Man', 10.0],
 ['Qatar', 'Panama', 24.0],
 ['Qatar', 'Seychelles', 2.0],
 ['Romania', 'British Anguilla', 4.0],
 ['Romania', 'British Virgin Islands', 5.0],
 ['Romania', 'Malta', 2.0],
 ['Romania', 'Panama', 4.0],
 ['Russia', 'Bahamas', 2670.0],
 ['Russia', 'Belize', 6.0],
 ['Russia', 'Bermuda', 50.0],
 ['Russia', 'British Anguilla', 146.0],
 ['Russia', 'British Virgin Islands', 4634.5],
 ['Russia', 'Cayman Islands', 10.5],
 ['Russia', 'Cook Islands', 4.0],
 ['Russia', 'Hong Kong', 8.5],
 ['Russia', 'Niue', 732.0],
 ['Russia', 'Panama', 188.0],
 ['Russia', 'Saint Kitts and Nevis', 2.0],
 ['Russia', 'Samoa', 12.0],
 ['Russia', 'Seychelles', 42.0],
 ['Russia', 'Singapore', 0.5],
 ['Russia', 'Uruguay', 4.0],
 ['Saint Kitts and Nevis', 'Bahamas', 18.0],
 ['Saint Kitts and Nevis', 'Barbados', 12.0],
 ['Saint Kitts and Nevis', 'British Virgin Islands', 52.5],
 ['Saint Kitts and Nevis', 'Cayman Islands', 2.0],
 ['Saint Kitts and Nevis', 'Cook Islands', 2.0],
 ['Saint Kitts and Nevis', 'Hong Kong', 28.0],
 ['Saint Kitts and Nevis', 'Mauritius', 2.0],
 ['Saint Kitts and Nevis', 'Nevada', 8.0],
 ['Saint Kitts and Nevis', 'Niue', 4.0],
 ['Saint Kitts and Nevis', 'Panama', 234.0],
 ['Saint Kitts and Nevis', 'Seychelles', 34.0],
 ['Saint Lucia', 'Barbados', 230.0],
 ['Saint Lucia', 'British Virgin Islands', 8.0],
 ['Saint Lucia', 'Panama', 2.0],
 ['Saint Lucia', 'Saint Lucia', 8.0],
 ['Saint Martin (French part)', 'Barbados', 2.0],
 ['Saint Vincent and the Grenadines', 'Barbados', 26.0],
 ['Saint Vincent and the Grenadines', 'British Virgin Islands', 6.0],
 ['Saint Vincent and the Grenadines', 'Panama', 6.0],
 ['Samoa', 'Bahamas', 242.0],
 ['Samoa', 'Bermuda', 2.0],
 ['Samoa', 'British Anguilla', 4.0],
 ['Samoa', 'British Virgin Islands', 501.0],
 ['Samoa', 'Cayman Islands', 4.0],
 ['Samoa', 'Cook Islands', 2.0],
 ['Samoa', 'Hong Kong', 32.0],
 ['Samoa', 'Niue', 156.0],
 ['Samoa', 'Panama', 320.0],
 ['Samoa', 'Seychelles', 1700.0],
 ['Samoa', 'United Kingdom', 2.0],
 ['Saudi Arabia', 'Bahamas', 8.0],
 ...]

d = collections.defaultdict(int)
for n1, n2, v in df_jur_grouped_list:
    d[min(n1, n2), max(n1, n2)] += v
result = [[k[0], k[1], v] for k, v in d.items()]

df_net = pd.DataFrame(result)
df_net

	0	1	2
0	Albania	Bahamas	4.0
1	Andorra	Bahamas	52.0
2	Andorra	British Virgin Islands	78.0
3	Andorra	Cayman Islands	2.0
4	Andorra	Cook Islands	0.5
...	...	...	...
1203	Panama	Vietnam	2.0
1204	Panama	Yemen	2.0
1205	British Virgin Islands	Zambia	4.0
1206	British Virgin Islands	Zimbabwe	10.0
1207	Panama	Zimbabwe	6.0

1208 rows × 3 columns

df_net = df_net.rename(columns = {0:'node_1', 1:'node_2', 2: 'weight'})
df_net

	node_1	node_2	weight
0	Albania	Bahamas	4.0
1	Andorra	Bahamas	52.0
2	Andorra	British Virgin Islands	78.0
3	Andorra	Cayman Islands	2.0
4	Andorra	Cook Islands	0.5
...	...	...	...
1203	Panama	Vietnam	2.0
1204	Panama	Yemen	2.0
1205	British Virgin Islands	Zambia	4.0
1206	British Virgin Islands	Zimbabwe	10.0
1207	Panama	Zimbabwe	6.0

1208 rows × 3 columns

#df_net.to_csv('undirected_weighted_grouped.csv', index = False)

df_net.describe()

	weight
count	1208.000000
mean	359.531871
std	2344.486509
min	0.500000
25%	2.000000
50%	9.250000
75%	52.500000
max	55256.500000

Create Graph

g = ig.Graph.TupleList(df_net.itertuples(index=False), directed=False, edge_attrs={'weight': "weight"})

ig.plot(g)

g.diameter()

4

print( "Clustering Coefficient: ", g.transitivity_undirected())

Clustering Coefficient:  0.2562554798678087

np.mean(g.degree())

13.805714285714286

np.mean(g.strength(weights = 'weight'))

4963.594285714285

Metrics

def get_diameter(community):
    
    diameters = []
    
    for i in range(len(community.subgraphs())):
    
        diameters.append(community.subgraphs()[i].diameter())

    return (diameters)

def get_transitivity(community):
    
    transitivity = []
    
    for i in range(len(community.subgraphs())):
    
        transitivity.append(community.subgraphs()[i].transitivity_undirected())

    return (transitivity)

def get_avg_degree(community):
    
    degree = []
    
    for i in range(len(community.subgraphs())):
    
        degree.append(np.mean(community.subgraphs()[i].degree()))

    return degree

def get_avg_weighted_degree(community):
    
    weighted_degree = []
    
    for i in range(len(community.subgraphs())):
    
        weighted_degree.append(np.mean(community.subgraphs()[i].strength(weights = 'weight')))

    return weighted_degree

Community Detection

Louvain Community Detection

comm_louvain = g.community_multilevel(weights = 'weight')

ig.plot(comm_louvain, mark_groups = True, layout="drl")

modularity = g.modularity(comm_louvain, weights='weight')
print(modularity)

0.1635560856610695

print('average weighted degree: ', get_avg_weighted_degree(comm_louvain))
print('average degree: ', get_avg_degree(comm_louvain))
print('transitivity: ', get_transitivity(comm_louvain))
print('diameter: ', get_diameter(comm_louvain))

average weighted degree:  [680.9230769230769, 3109.3333333333335, 5573.571428571428, 370.0681818181818, 538.0454545454545, 845.0]
average degree:  [2.0, 4.711111111111111, 4.040816326530612, 5.318181818181818, 2.3636363636363638, 1.0]
transitivity:  [0.04411764705882353, 0.19753846153846155, 0.18665811417575368, 0.25, 0.07407407407407407, nan]
diameter:  [2, 3, 3, 4, 3, 1]

df_louvain = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_louvain.membership } )
df_louvain

	name	community
0	Albania	0
1	Bahamas	0
2	Andorra	1
3	British Virgin Islands	2
4	Cayman Islands	3
...	...	...
170	Venezuela	1
171	Vietnam	3
172	Yemen	1
173	Zambia	2
174	Zimbabwe	2

175 rows × 2 columns

df_louvain.describe()

	community
count	175.000000
mean	2.131429
std	1.184132
min	0.000000
25%	1.000000
50%	2.000000
75%	3.000000
max	5.000000

df_louvain[df_louvain['community'] == 0]

	name	community
0	Albania	0
1	Bahamas	0
28	Azerbaijan	0
29	Cyprus	0
33	Wyoming	0
73	Denmark	0
82	Finland	0
131	Nigeria	0
139	Poland	0
144	Russia	0
158	Sweden	0
160	Syria	0
167	Ukraine	0

df_louvain[df_louvain['community'] == 1].head(99)

	name	community
2	Andorra	1
6	Costa Rica	1
8	Nevada	1
9	Niue	1
10	Panama	1
14	Uruguay	1
20	Argentina	1
38	Belgium	1
39	Belize	1
48	Bolivia	1
50	Brazil	1
60	Luxembourg	1
65	Chile	1
67	Colombia	1
72	Côte d'Ivoire	1
74	Djibouti	1
75	Dominica	1
76	Dominican Republic	1
77	Ecuador	1
78	Egypt	1
79	El Salvador	1
86	Germany	1
88	Greece	1
90	Guatemala	1
92	Haiti	1
93	Honduras	1
100	Iran	1
109	Kuwait	1
114	Libya	1
115	Lithuania	1
121	Mexico	1
123	Monaco	1
129	Nauru	1
130	Nicaragua	1
136	Paraguay	1
137	Peru	1
140	Portugal	1
142	Qatar	1
149	Sint Maarten (Dutch part)	1
150	Slovakia	1
154	Spain	1
156	Sudan	1
159	Switzerland	1
170	Venezuela	1
172	Yemen	1

df_louvain[df_louvain['community'] == 2]

	name	community
3	British Virgin Islands	2
7	Hong Kong	2
13	United Kingdom	2
15	Angola	2
22	Aruba	2
25	Isle of Man	2
30	Malta	2
37	Belarus	2
40	Mauritius	2
41	Jersey	2
43	Liechtenstein	2
45	Singapore	2
53	Brunei	2
57	Gibraltar	2
59	Guernsey	2
63	Central African Republic	2
64	Chad	2
66	China	2
69	Cuba	2
71	Czech Republic	2
80	Estonia	2
89	Guam	2
95	Vanuatu	2
97	Iceland	2
99	Indonesia	2
101	Ireland	2
106	Jordan	2
107	Kazakhstan	2
108	Kenya	2
110	Kyrgyzstan	2
111	Latvia	2
116	Macao	2
118	Malawi	2
120	Mali	2
122	Moldova	2
124	Mongolia	2
125	Montenegro	2
132	Northern Mariana Islands	2
134	Oman	2
152	South Africa	2
155	Sri Lanka	2
161	Taiwan	2
162	Tanzania	2
164	Tunisia	2
165	Turkey	2
166	Uganda	2
169	Uzbekistan	2
173	Zambia	2
174	Zimbabwe	2

df_louvain[df_louvain['community'] == 3]

	name	community
4	Cayman Islands	3
5	Cook Islands	3
11	Samoa	3
16	Bermuda	3
17	Antigua and Barbuda	3
18	Barbados	3
19	Saint Kitts and Nevis	3
21	New Zealand	3
24	Australia	3
27	Austria	3
31	Netherlands Antilles	3
32	Saint Lucia	3
34	Bahrain	3
36	Bangladesh	3
42	Liberia	3
44	Netherlands	3
46	Turks and Caicos Islands	3
47	United States	3
51	Marshall Islands	3
52	State of Delaware	3
56	Canada	3
58	Grenada	3
61	Saint Vincent and the Grenadines	3
62	US Virgin Islands	3
68	Croatia	3
70	Curaçao	3
81	Fiji	3
83	France	3
84	French Guiana	3
91	Guyana	3
98	India	3
104	Jamaica	3
105	Japan	3
133	Norway	3
135	Pakistan	3
138	Philippines	3
141	Puerto Rico	3
145	Saint Martin (French part)	3
146	Saudi Arabia	3
148	Sierra Leone	3
153	South Korea	3
157	Suriname	3
163	Trinidad and Tobago	3
171	Vietnam	3

df_louvain[df_louvain['community'] == 4]

	name	community
12	Seychelles	4
23	British Anguilla	4
35	United Arab Emirates	4
49	Botswana	4
54	Bulgaria	4
55	Cameroon	4
85	Georgia	4
87	Ghana	4
94	Thailand	4
96	Hungary	4
102	Israel	4
103	Italy	4
112	Lebanon	4
113	Lesotho	4
117	Macedonia	4
126	Morocco	4
127	Mozambique	4
128	Namibia	4
143	Romania	4
147	Senegal	4
151	Slovenia	4
168	Dubai	4

# Define colors used for visualization
colors = ['blue', 'yellow', 'green','purple', 'pink', 'orange', 'lightcoral', 'plum', 'darkgreen', 'darkblue', 'rosybrown',
         'crimson', 'steelblue', 'khaki', 'moccasin', 'orangered', 'fuchsia', 'thistle', 'salmon', 'chocolate', 'lightseagreen',
         'springgreen', 'darkseagreen', 'deeppink', 'mediumvioletred','olive']

##plot community
visual_style = {}

vertex_colors = []

for i in range(len(comm_louvain.membership)):
    vertex_colors.append(colors[comm_louvain.membership[i]])
    
# Set bbox and margin
visual_style["bbox"] = (5000,5000)
visual_style["margin"] = 17

# Set vertex colours
visual_style["vertex_color"] = vertex_colors

# Set edge width
visual_style["edge_width"] = np.log(g.es['weight']) + 1

# Set edge color
visual_style["edge_color"] = "rgba(1,1,1,0.1)" 

# Set vertex size
visual_style["vertex_size"] = 15

# Set vertex label size
visual_style["vertex_label_size"] = 20

# Set vertex label color
visual_style["vertex_label_color"] = 'red'

# Don't curve the edges
visual_style["edge_curved"] = False

# Set the layout
my_layout = g.layout_fruchterman_reingold()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'jur_all_images/louvain_communities.png', vertex_label = g.vs['name'] , **visual_style)

# Set the layout
my_layout = g.layout_circle()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'jur_all_images/louvain_circular.png', vertex_label = g.vs['name'] , **visual_style)

for i in range(len(comm_louvain.subgraphs())):
    
    visual_style["bbox"] = (2000,2000)
    visual_style["margin"] = 17
    
    # Set vertex colours
    visual_style["vertex_color"] = 'green'

    # Set edge width
    visual_style["edge_width"] = np.log(comm_louvain.subgraphs()[i].es['weight']) + 1
    # Set the layout
    my_layout = comm_louvain.subgraphs()[i].layout_fruchterman_reingold()
    visual_style["layout"] = my_layout

    ig.plot(comm_louvain.subgraphs()[i],'jur_all_images/louvain_subgraph' + str(i) + '.png' ,vertex_label = comm_louvain.subgraphs()[i].vs['name'],**visual_style  )
    
    
    

# # Set vertex colours

# visual_style["bbox"] = (300,300)
# visual_style["margin"] = 17
# visual_style["vertex_color"] = 'orange'

# # Set vertex label size
# visual_style["vertex_label_size"] = 15

# # Set the layout
# my_layout = comm_louvain.cluster_graph().layout_fruchterman_reingold()
# visual_style["layout"] = my_layout

# ig.plot(comm_louvain.cluster_graph(),'jur_all_images/test_cluster_graph.png' ,vertex_label = range(comm_louvain.cluster_graph().vcount()),**visual_style  )

Label Propogation Community Detection

comm_lp= g.community_label_propagation(weights = 'weight')

ig.plot(comm_lp, mark_groups = True, layout="drl")

print('average weighted degree: ', get_avg_weighted_degree(comm_lp))
print('average degree: ', get_avg_degree(comm_lp))
print('transitivity: ', get_transitivity(comm_lp))
print('diameter: ', get_diameter(comm_lp))

average weighted degree:  [5008.439306358381, 845.0]
average degree:  [13.641618497109826, 1.0]
transitivity:  [0.25755787719968065, nan]
diameter:  [4, 1]

modularity = g.modularity(comm_lp, weights='weight')
print(modularity)

0.003881320954418831

print('average weighted degree: ', get_avg_weighted_degree(comm_louvain))
print('average degree: ', get_avg_degree(comm_louvain))
print('average transitivity: ', get_avg_transitivity(comm_louvain))
print('average diameter: ', get_avg_diameter(comm_louvain))

average weighted degree:  1852.823579198579
average degree:  3.238957603243317
average transitivity:  0.15047765936942256
average diameter:  2.6666666666666665

Leading Eigenvector Community Detection

comm_leading_eigenvector= g.community_leading_eigenvector(weights = 'weight')

ig.plot(comm_leading_eigenvector, mark_groups = True, layout="drl")

modularity = g.modularity(comm_leading_eigenvector, weights='weight')
print(modularity)

0.1329364769372975

print('average weighted degree: ', get_avg_weighted_degree(comm_leading_eigenvector))
print('average degree: ', get_avg_degree(comm_leading_eigenvector))
print('transitivity: ', get_transitivity(comm_leading_eigenvector))
print('diameter: ', get_diameter(comm_leading_eigenvector))

average weighted degree:  [244.5, 5217.266666666666, 2703.529411764706, 8.0, 0.0, 0.0, 845.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 16.0, 0.0, 0.0, 0.0, 0.0]
average degree:  [2.5, 9.013333333333334, 3.803921568627451, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0]
transitivity:  [0.14285714285714285, 0.3622222222222222, 0.131175468483816, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
diameter:  [3, 2, 2, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

df_leading_eigenvector = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_leading_eigenvector.membership } )
df_leading_eigenvector

	name	community
0	Albania	0
1	Bahamas	0
2	Andorra	2
3	British Virgin Islands	1
4	Cayman Islands	1
...	...	...
170	Venezuela	2
171	Vietnam	1
172	Yemen	2
173	Zambia	1
174	Zimbabwe	2

175 rows × 2 columns

df_leading_eigenvector.describe()

	community
count	175.000000
mean	3.720000
std	5.855187
min	0.000000
25%	1.000000
50%	1.000000
75%	2.000000
max	24.000000

##plot community
visual_style = {}

vertex_colors = []

for i in range(len(comm_leading_eigenvector.membership)):
    vertex_colors.append(colors[comm_leading_eigenvector.membership[i]])
    
# Set bbox and margin
visual_style["bbox"] = (5000,5000)
visual_style["margin"] = 17

# Set vertex colours
visual_style["vertex_color"] = vertex_colors

# Set edge width
visual_style["edge_width"] = np.log(g.es['weight']) + 1

# Set edge color
visual_style["edge_color"] = "rgba(1,1,1,0.1)" 

# Set vertex size
visual_style["vertex_size"] = 15

# Set vertex label size
visual_style["vertex_label_size"] = 20

# Set vertex label color
visual_style["vertex_label_color"] = 'red'

# Don't curve the edges
visual_style["edge_curved"] = False

# Set the layout
my_layout = g.layout_fruchterman_reingold()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'jur_all_images/communities_leading_eigenvector.png', vertex_label = g.vs['name'] , **visual_style)

# Set the layout
my_layout = g.layout_circle()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'jur_all_images/leading_vector_circular.png', vertex_label = g.vs['name'] , **visual_style)

for i in range(len(comm_leading_eigenvector.subgraphs())):
    
    visual_style["bbox"] = (2000,2000)
    visual_style["margin"] = 17
    
    # Set vertex colours
    visual_style["vertex_color"] = 'green'

    # Set edge width
    visual_style["edge_width"] = np.log(comm_leading_eigenvector.subgraphs()[i].es['weight']) + 1
    # Set the layout
    my_layout = comm_leading_eigenvector.subgraphs()[i].layout_fruchterman_reingold()
    visual_style["layout"] = my_layout

    ig.plot(comm_leading_eigenvector.subgraphs()[i],'jur_all_images/leading_eigenvector_subgraph' + str(i) + '.png' ,vertex_label = comm_leading_eigenvector.subgraphs()[i].vs['name'],**visual_style  )
    

Leiden Community Detection

comm_leiden = g.community_leiden(weights = 'weight')

ig.plot(comm_leiden, mark_groups = True, layout="drl")

modularity = g.modularity(comm_leiden, weights='weight')
print(modularity)

5.207166225002193e-06

print('average weighted degree: ', get_avg_weighted_degree(comm_leiden))
print('average degree: ', get_avg_degree(comm_leiden))
print('transitivity: ', get_transitivity(comm_leiden))
print('diameter: ', get_diameter(comm_leiden))

average weighted degree:  [0.0, 9303.258064516129, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
average degree:  [0.0, 20.602150537634408, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
transitivity:  [nan, 0.3711734693877551, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
diameter:  [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

df_leiden = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_leiden.membership } )
df_leiden

	name	community
0	Albania	0
1	Bahamas	1
2	Andorra	1
3	British Virgin Islands	1
4	Cayman Islands	1
...	...	...
170	Venezuela	1
171	Vietnam	79
172	Yemen	80
173	Zambia	81
174	Zimbabwe	82

175 rows × 2 columns

df_leiden.describe()

	community
count	175.000000
mean	19.971429
std	25.981521
min	0.000000
25%	1.000000
50%	1.000000
75%	38.500000
max	82.000000

## no visualisation as most communities are individual?

Optimal Modularity Community Detection

# #SLOW

# comm_opt_modularity = g.community_optimal_modularity(weights = 'weight')

# ig.plot(comm_opt_modularity, mark_groups = True, layout="drl")

# modularity = g.modularity(comm_opt_modularity, weights='weight')
# print(modularity)

# df_opt_modularity = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_opt_modularity.membership } )
# df_opt_modularity

# df_opt_modularity.describe

# ##plot community
# visual_style = {}

# vertex_colors = []

# for i in range(len(df_opt_modularity.membership)):
#     vertex_colors.append(colors[df_opt_modularity.membership[i]])
    
# # Set bbox and margin
# visual_style["bbox"] = (5000,5000)
# visual_style["margin"] = 17

# # Set vertex colours
# visual_style["vertex_color"] = vertex_colors

# # Set edge width
# visual_style["edge_width"] = np.log(g.es['weight']) + 1

# # Set edge color
# visual_style["edge_color"] = "rgba(1,1,1,0.1)" 

# # Set vertex size
# visual_style["vertex_size"] = 15

# # Set vertex label size
# visual_style["vertex_label_size"] = 20

# # Set vertex label color
# visual_style["vertex_label_color"] = 'red'

# # Don't curve the edges
# visual_style["edge_curved"] = False

# # Set the layout
# my_layout = g.layout_fruchterman_reingold()
# visual_style["layout"] = my_layout

# # Plot the graph
# ig.plot(g,'jur_all_images/communities_optimal_modularity.png', vertex_label = g.vs['name'] , **visual_style)

# # Set the layout
# my_layout = g.layout_circle()
# visual_style["layout"] = my_layout

# # Plot the graph
# ig.plot(g,'jur_all_images/optimal_modularity_circular.png', vertex_label = g.vs['name'] , **visual_style)

# for i in range(len(optimal_modularity.subgraphs())):
    
#     visual_style["bbox"] = (2000,2000)
#     visual_style["margin"] = 17
    
#     # Set vertex colours
#     visual_style["vertex_color"] = 'green'

#     # Set edge width
#     visual_style["edge_width"] = np.log(optimal_modularity.subgraphs()[i].es['weight']) + 1
#     # Set the layout
#     my_layout = optimal_modularity_circular.subgraphs()[i].layout_fruchterman_reingold()
#     visual_style["layout"] = my_layout

#     ig.plot(optimal_modularity_circular.subgraphs()[i],'jur_all_images/optimal_modularity_subgraph' + str(i) + '.png' ,vertex_label = optimal_modularity.subgraphs()[i].vs['name'],**visual_style  )
    

Spinglass Community Detection

comm_spinglass = g.community_spinglass(weights = 'weight')

ig.plot(comm_spinglass, mark_groups = True, layout="drl")

modularity = g.modularity(comm_spinglass, weights='weight')
print(modularity)

0.16465889687970514

print('average weighted degree: ', get_avg_weighted_degree(comm_spinglass))
print('average degree: ', get_avg_degree(comm_spinglass))
print('transitivity: ', get_transitivity(comm_spinglass))
print('diameter: ', get_diameter(comm_spinglass))

average weighted degree:  [845.0, 298.6744186046512, 5479.375, 3109.3333333333335, 632.5714285714286, 886.6086956521739]
average degree:  [1.0, 4.837209302325581, 3.8333333333333335, 4.711111111111111, 2.0, 3.130434782608696]
transitivity:  [nan, 0.21428571428571427, 0.1732394366197183, 0.19753846153846155, 0.0375, 0.2125984251968504]
diameter:  [1, 4, 3, 3, 2, 3]

df_spinglass = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_spinglass.membership } )
df_spinglass

	name	community
0	Albania	4
1	Bahamas	4
2	Andorra	3
3	British Virgin Islands	2
4	Cayman Islands	1
...	...	...
170	Venezuela	3
171	Vietnam	1
172	Yemen	3
173	Zambia	2
174	Zimbabwe	2

175 rows × 2 columns

df_spinglass.describe()

	community
count	175.000000
mean	2.542857
std	1.329222
min	0.000000
25%	1.000000
50%	2.000000
75%	3.000000
max	5.000000

##plot community
visual_style = {}

vertex_colors = []

for i in range(len(comm_spinglass.membership)):
    vertex_colors.append(colors[comm_spinglass.membership[i]])
    
# Set bbox and margin
visual_style["bbox"] = (5000,5000)
visual_style["margin"] = 17

# Set vertex colours
visual_style["vertex_color"] = vertex_colors

# Set edge width
visual_style["edge_width"] = np.log(g.es['weight']) + 1

# Set edge color
visual_style["edge_color"] = "rgba(1,1,1,0.1)" 

# Set vertex size
visual_style["vertex_size"] = 15

# Set vertex label size
visual_style["vertex_label_size"] = 20

# Set vertex label color
visual_style["vertex_label_color"] = 'red'

# Don't curve the edges
visual_style["edge_curved"] = False

# Set the layout
my_layout = g.layout_fruchterman_reingold()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'jur_all_images/communities_spinglass.png', vertex_label = g.vs['name'] , **visual_style)

# Set the layout
my_layout = g.layout_circle()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'jur_all_images/spinglass_circular.png', vertex_label = g.vs['name'] , **visual_style)

for i in range(len(comm_spinglass.subgraphs())):
    
    visual_style["bbox"] = (2000,2000)
    visual_style["margin"] = 17
    
    # Set vertex colours
    visual_style["vertex_color"] = 'green'

    # Set edge width
    visual_style["edge_width"] = np.log(comm_spinglass.subgraphs()[i].es['weight']) + 1
    # Set the layout
    my_layout = comm_spinglass.subgraphs()[i].layout_fruchterman_reingold()
    visual_style["layout"] = my_layout

    ig.plot(comm_spinglass.subgraphs()[i],'jur_all_images/spinglass_subgraph' + str(i) + '.png' ,vertex_label = comm_spinglass.subgraphs()[i].vs['name'],**visual_style  )
    

Walktrap Community Detection

walktrap = g.community_walktrap(weights = 'weight', steps = 2)

comm_walktrap = walktrap.as_clustering()

ig.plot(comm_walktrap, mark_groups = True, layout="drl")

modularity = g.modularity(comm_walktrap, weights='weight')
print(modularity)

0.004762528335317103

print('average weighted degree: ', get_avg_weighted_degree(comm_walktrap))
print('average degree: ', get_avg_degree(comm_walktrap))
print('transitivity: ', get_transitivity(comm_walktrap))
print('diameter: ', get_diameter(comm_walktrap))

average weighted degree:  [0.0, 8222.567307692309, 0.0, 114.18181818181819, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
average degree:  [0.0, 18.28846153846154, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
transitivity:  [nan, 0.3268184256917299, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]
diameter:  [0, 3, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

df_walktrap = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_walktrap.membership } )
df_walktrap

	name	community
0	Albania	0
1	Bahamas	1
2	Andorra	1
3	British Virgin Islands	1
4	Cayman Islands	1
...	...	...
170	Venezuela	1
171	Vietnam	1
172	Yemen	59
173	Zambia	60
174	Zimbabwe	61

175 rows × 2 columns

df_walktrap.describe()

	community
count	175.000000
mean	11.565714
std	17.667913
min	0.000000
25%	1.000000
50%	1.000000
75%	17.500000
max	61.000000

## no visualisation since too many communities, mostly individual

Infomap Community Detection

comm_infomap = g.community_infomap(edge_weights = 'weight')

ig.plot(comm_infomap, mark_groups = True, layout="drl")

modularity = g.modularity(comm_infomap, weights='weight')
print(modularity)

0.003881320954418831

print('average weighted degree: ', get_avg_weighted_degree(comm_infomap))
print('average degree: ', get_avg_degree(comm_infomap))
print('transitivity: ', get_transitivity(comm_infomap))
print('diameter: ', get_diameter(comm_infomap))

average weighted degree:  [5008.439306358381, 845.0]
average degree:  [13.641618497109826, 1.0]
transitivity:  [0.25755787719968065, nan]
diameter:  [4, 1]

df_infomap = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_infomap.membership } )
df_infomap

	name	community
0	Albania	0
1	Bahamas	0
2	Andorra	0
3	British Virgin Islands	0
4	Cayman Islands	0
...	...	...
170	Venezuela	0
171	Vietnam	0
172	Yemen	0
173	Zambia	0
174	Zimbabwe	0

175 rows × 2 columns

## no visualisation since no community is detected

Fast Greedy Community Detection

fg = g.community_fastgreedy(weights = 'weight')

comm_fastgreedy = fg.as_clustering()

ig.plot(comm_fastgreedy, mark_groups = True, layout="drl")

modularity = g.modularity(comm_fastgreedy, weights='weight')
print(modularity)

0.16289420101025118

print('average weighted degree: ', get_avg_weighted_degree(comm_fastgreedy)) print('average degree: ', get_avg_degree(comm_fastgreedy)) print('transitivity: ', get_transitivity(comm_fastgreedy)) print('diameter: ', get_diameter(comm_fastgreedy))

df_fastgreedy = pd.DataFrame({'name': list(g.vs['name']), 'community':comm_fastgreedy.membership } )
df_fastgreedy

	name	community
0	Albania	0
1	Bahamas	0
2	Andorra	1
3	British Virgin Islands	2
4	Cayman Islands	3
...	...	...
170	Venezuela	1
171	Vietnam	3
172	Yemen	1
173	Zambia	2
174	Zimbabwe	2

175 rows × 2 columns

df_fastgreedy.describe()

	community
count	175.000000
mean	2.120000
std	1.199808
min	0.000000
25%	1.000000
50%	2.000000
75%	3.000000
max	5.000000

##plot community
visual_style = {}

vertex_colors = []

for i in range(len(comm_fastgreedy.membership)):
    vertex_colors.append(colors[comm_fastgreedy.membership[i]])
    
# Set bbox and margin
visual_style["bbox"] = (5000,5000)
visual_style["margin"] = 17

# Set vertex colours
visual_style["vertex_color"] = vertex_colors

# Set edge width
visual_style["edge_width"] = np.log(g.es['weight']) + 1

# Set edge color
visual_style["edge_color"] = "rgba(1,1,1,0.1)" 

# Set vertex size
visual_style["vertex_size"] = 15

# Set vertex label size
visual_style["vertex_label_size"] = 20

# Set vertex label color
visual_style["vertex_label_color"] = 'red'

# Don't curve the edges
visual_style["edge_curved"] = False

# Set the layout
my_layout = g.layout_fruchterman_reingold()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'jur_all_images/communities_fastgreedy.png', vertex_label = g.vs['name'] , **visual_style)

# Set the layout
my_layout = g.layout_circle()
visual_style["layout"] = my_layout

# Plot the graph
ig.plot(g,'jur_all_images/fastgreedy_circular.png', vertex_label = g.vs['name'] , **visual_style)

for i in range(len(comm_fastgreedy.subgraphs())):
    
    visual_style["bbox"] = (2000,2000)
    visual_style["margin"] = 17
    
    # Set vertex colours
    visual_style["vertex_color"] = 'green'

    # Set edge width
    visual_style["edge_width"] = np.log(comm_fastgreedy.subgraphs()[i].es['weight']) + 1
    # Set the layout
    my_layout = comm_fastgreedy.subgraphs()[i].layout_fruchterman_reingold()
    visual_style["layout"] = my_layout

    ig.plot(comm_fastgreedy.subgraphs()[i],'jur_all_images/fastgreedy_subgraph' + str(i) + '.png' ,vertex_label = comm_fastgreedy.subgraphs()[i].vs['name'],**visual_style  )
    

get_avg_diameter(comm_fastgreedy)

2.6666666666666665

get_avg_transitivity(comm_fastgreedy)

0.14719170395542702